Near infrared (NIR) spectroscopy has the capability of providing real-time, multi-analyte monitoring of the complex reaction mixture associated with cell culture processes. However, the development of robust models to predict the concentration of key analytes has proven difficult. In this study, a modeling methodology using semisynthetic process samples was used to predict glucose concentrations in Chinese Hamster Ovary (CHO) cell culture processes. Partial Least Squares (PLS) regression models were built from in situ NIR spectra, and glucose levels between 4.0 and 14.0 g/L. Two models were constructed. The “standard model” used data provided by cell culture production process samples. The “full model” included the data provided from both cell culture production process samples and semisynthetic samples. The semisynthetic samples were generated by titrating cell culture samples with target viable cell density (VCD) and lactate levels to defined glucose concentrations. The robustness of each model was gauged by predicting glucose in a subsequent cell culture process utilizing a media formulation and cell line not contained in the calibration data sets. The “full model” generated glucose predictions with a root mean square error of prediction (RMSEP) of 0.99 g/L while the “standard model” provided glucose predictions with a RMSEP of 2.26 g/L. The modeling approach utilizing semisynthetic samples proved to be faster development and more effective than using just standard cell culture processes. Biotechnol. Biotechnol. Bioeng. 2014;111: 896–903. © 2013 Wiley Periodicals, Inc.